A Configurable Architecture for
Sparse LU Decomposition on Matrices with Arbitrary Patterns

(Feb. 2014 - Dec. 2014)

Presented an efficient architecture
for sparse LU decomposition, which can analyze both symmetric
and unsymmetric sparse matrices with arbitrary sparsity
patterns.

The architecture parallelized the
computations and pivoting operations with its control logic and
resource usage can be configured based on the property of input
matrices.

A Reconfigurable Architecture for QR Decomposition
Using A Hybrid Approach

(Feb. 2013 - Feb. 2014)

Proposed a deeply pipelined
reconfigurable architecture that can dynamically configured to
perform either Householder transformation or Givens rotation in
a manner that takes advantage of the strengths of each.

At runtime, the input matrix is first
partitioned into numerous submatrices. Then, parallel
Householder transformations on the sub-matrices in the same
column block are performed, which is followed by parallel Givens
rotations to annihilate the remaining unneeded individual
off-diagonals.

Demonstrated improved efficiency of our
architecture compared to an optimized software-based SVD
solution for matrices with small to medium column dimensions,
even with comparably large row dimensions.